def cache_segregation():
# Segregation index
tot_pct_white = get_pop_weighted_characteristic("pct_whitenh",\
tracts=CensusTract.objects.all())
tot_pct_black = get_pop_weighted_characteristic("pct_blacknh",\
tracts=CensusTract.objects.all())
tot_pct_hisp = get_pop_weighted_characteristic("pct_hispanic",\
tracts=CensusTract.objects.all())
tot_pct_asian = get_pop_weighted_characteristic("pct_asiannh",\
tracts=CensusTract.objects.all())
# We'll define it as the percentage of people who would have to
# move in order for the geometry to match Cook County as a whole.
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
pct_white = get_pop_weighted_characteristic("pct_whitenh",\
geoms=geom)
pct_black = get_pop_weighted_characteristic("pct_blacknh",\
geoms=geom)
pct_hisp = get_pop_weighted_characteristic("pct_hispanic",\
geoms=geom)
pct_asian = get_pop_weighted_characteristic("pct_asiannh",\
geoms=geom)
segregation = (\
abs(tot_pct_white-pct_white)+\
abs(tot_pct_black-pct_black)+\
abs(tot_pct_hisp-pct_hisp)+\
abs(tot_pct_asian-pct_asian))/2.0
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name = 'segregation',\
indicator_value = segregation)
cv.save()
def cache_segregation():
# Segregation index
tot_pct_white = get_pop_weighted_characteristic("pct_whitenh",\
tracts=CensusTract.objects.all())
tot_pct_black = get_pop_weighted_characteristic("pct_blacknh",\
tracts=CensusTract.objects.all())
tot_pct_hisp = get_pop_weighted_characteristic("pct_hispanic",\
tracts=CensusTract.objects.all())
tot_pct_asian = get_pop_weighted_characteristic("pct_asiannh",\
tracts=CensusTract.objects.all())
# We'll define it as the percentage of people who would have to
# move in order for the geometry to match Cook County as a whole.
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
pct_white = get_pop_weighted_characteristic("pct_whitenh",\
geoms=geom)
pct_black = get_pop_weighted_characteristic("pct_blacknh",\
geoms=geom)
pct_hisp = get_pop_weighted_characteristic("pct_hispanic",\
geoms=geom)
pct_asian = get_pop_weighted_characteristic("pct_asiannh",\
geoms=geom)
segregation = (\
abs(tot_pct_white-pct_white)+\
abs(tot_pct_black-pct_black)+\
abs(tot_pct_hisp-pct_hisp)+\
abs(tot_pct_asian-pct_asian))/2.0
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name = 'segregation',\
indicator_value = segregation)
cv.save()
def cache_demolition_indicator():
lastyear = cst.localize(datetime.datetime.now() - datetime.timedelta(days=365.25))
city_geom=Municipality.objects.get(name='Chicago').geom
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
pop = IndicatorCache.objects.get(indicator_name='pop', \
area_type=geom_str, area_id=geom.id).indicator_value
if pop==0: continue
if (geom_type==CensusTract):
if not city_geom.intersects(geom.loc): continue
myinds = BuildingPermit.objects.filter(loc__within=geom.loc).\
filter(timestamp__gte=lastyear).\
filter(permit_type__exact='PERMIT - WRECKING/DEMOLITION')
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='demolitions_pc',\
indicator_value = float(len(myinds))/pop,\
indicator_date = lastyear)
cv.save()
else:
if not city_geom.intersects(geom.geom): continue
if geom_type==Municipality and geom.name!='Chicago': continue
myinds = BuildingPermit.objects.filter(loc__within=geom.geom).\
filter(timestamp__gte=lastyear).\
filter(permit_type__exact='PERMIT - WRECKING/DEMOLITION')
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='demolitions_pc',\
indicator_value = float(len(myinds))/pop,\
indicator_date = lastyear)
cv.save()
def cache_vacancy311_indicator():
lastyear = cst.localize(datetime.datetime.now() -
datetime.timedelta(days=365.25))
city_geom = Municipality.objects.get(name='Chicago').geom
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
if (geom_type == CensusTract):
if not city_geom.intersects(geom.loc): continue
myinds = Vacancy311.objects.filter(loc__within=geom.loc).\
filter(request_date__gte=lastyear)
assess = Assessor.objects.filter(loc__contained=geom.loc)
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='vacancy_311',\
indicator_value = float(len(myinds))/len(assess)*100,\
indicator_date = lastyear)
cv.save()
else:
if not city_geom.intersects(geom.geom): continue
if geom_type == Municipality and geom.name != 'Chicago':
continue
myinds = Vacancy311.objects.filter(loc__within=geom.geom).\
filter(request_date__gte=lastyear)
assess = Assessor.objects.filter(loc__contained=geom.geom)
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='vacancy_311',\
indicator_value = float(len(myinds))/len(assess)*100,\
indicator_date = lastyear)
cv.save()
def cache_demolition_indicator():
lastyear = cst.localize(datetime.datetime.now() -
datetime.timedelta(days=365.25))
city_geom = Municipality.objects.get(name='Chicago').geom
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
pop = IndicatorCache.objects.get(indicator_name='pop', \
area_type=geom_str, area_id=geom.id).indicator_value
if pop == 0: continue
if (geom_type == CensusTract):
if not city_geom.intersects(geom.loc): continue
myinds = BuildingPermit.objects.filter(loc__within=geom.loc).\
filter(timestamp__gte=lastyear).\
filter(permit_type__exact='PERMIT - WRECKING/DEMOLITION')
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='demolitions_pc',\
indicator_value = float(len(myinds))/pop,\
indicator_date = lastyear)
cv.save()
else:
if not city_geom.intersects(geom.geom): continue
if geom_type == Municipality and geom.name != 'Chicago':
continue
myinds = BuildingPermit.objects.filter(loc__within=geom.geom).\
filter(timestamp__gte=lastyear).\
filter(permit_type__exact='PERMIT - WRECKING/DEMOLITION')
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='demolitions_pc',\
indicator_value = float(len(myinds))/pop,\
indicator_date = lastyear)
cv.save()
def cache_vacancy311_indicator():
lastyear = cst.localize(datetime.datetime.now() - datetime.timedelta(days=365.25))
city_geom=Municipality.objects.get(name='Chicago').geom
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
if (geom_type==CensusTract):
if not city_geom.intersects(geom.loc): continue
myinds = Vacancy311.objects.filter(loc__within=geom.loc).\
filter(request_date__gte=lastyear)
assess = Assessor.objects.filter(loc__contained=geom.loc)
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='vacancy_311',\
indicator_value = float(len(myinds))/len(assess)*100,\
indicator_date = lastyear)
cv.save()
else:
if not city_geom.intersects(geom.geom): continue
if geom_type==Municipality and geom.name!='Chicago': continue
myinds = Vacancy311.objects.filter(loc__within=geom.geom).\
filter(request_date__gte=lastyear)
assess = Assessor.objects.filter(loc__contained=geom.geom)
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='vacancy_311',\
indicator_value = float(len(myinds))/len(assess)*100,\
indicator_date = lastyear)
cv.save()
def cache_population():
# Total population.
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name = 'pop',\
indicator_value = get_population(geoms=geom))
cv.save()
def cache_population():
# Total population.
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name = 'pop',\
indicator_value = get_population(geoms=geom))
cv.save()
def cache_vacancyusps_indicator():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
print '.'
retval = get_vacancyusps(geoms=geom)
for k,v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='vacancy_usps',\
indicator_value = v*100.0,\
indicator_date = quarter_to_datetime(k))
cv.save()
def cache_vacancyusps_indicator():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
print '.'
retval = get_vacancyusps(geoms=geom)
for k, v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='vacancy_usps',\
indicator_value = v*100.0,\
indicator_date = quarter_to_datetime(k))
cv.save()
def cache_income():
for field in [\
'inc_lt_10', 'inc_10_15', 'inc_15_25', 'inc_25_35',\
'inc_35_50', 'inc_50_75', 'inc_75_100', 'inc_100_150',\
'inc_150_200', 'inc_gt_200', 'med_inc']:
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
val = get_pop_weighted_characteristic(field,\
geoms=geom, income=True)
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name = field,\
indicator_value = val)
cv.save()
def cache_income():
for field in [\
'inc_lt_10', 'inc_10_15', 'inc_15_25', 'inc_25_35',\
'inc_35_50', 'inc_50_75', 'inc_75_100', 'inc_100_150',\
'inc_150_200', 'inc_gt_200', 'med_inc']:
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
val = get_pop_weighted_characteristic(field,\
geoms=geom, income=True)
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name = field,\
indicator_value = val)
cv.save()
def cache_census():
# Other census indicators.
for indicator in ['median_age', 'pct_18plus', 'pct_65plus', 'pct_whitenh',\
'pct_blacknh', 'pct_asiannh', 'pct_hispanic', 'pct_owner_occupied',\
'pct_occ_units',\
'pct_renter_occupied', 'owner_occ_hh_size', 'renter_occ_hh_size']:
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
retval = get_pop_weighted_characteristic(indicator, geoms=geom)
cv = IndicatorCache(\
area_type=geom_str,\
area_id = geom.id,\
indicator_name = indicator,\
indicator_value = retval)
cv.save()
def cache_accessibility_indicators():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
geomloc = geom.loc if geom_type==CensusTract else geom.geom
blocks = CensusBlock.objects.filter(loc__dwithin=(geomloc, 5280))
retval = 0.0
for block in blocks:
for emp in block.censusblockemployment_set.all(): retval += emp.jobs
pop = IndicatorCache.objects.get(indicator_name='pop', \
area_type=geom_str, area_id=geom.id).indicator_value
if pop==0: continue
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='jobs_within_mile_pc',\
indicator_value = retval/float(pop))
cv.save()
def cache_census():
# Other census indicators.
for indicator in ['median_age', 'pct_18plus', 'pct_65plus', 'pct_whitenh',\
'pct_blacknh', 'pct_asiannh', 'pct_hispanic', 'pct_owner_occupied',\
'pct_occ_units',\
'pct_renter_occupied', 'owner_occ_hh_size', 'renter_occ_hh_size']:
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
retval = get_pop_weighted_characteristic(indicator, geoms=geom)
cv = IndicatorCache(\
area_type=geom_str,\
area_id = geom.id,\
indicator_name = indicator,\
indicator_value = retval)
cv.save()
def cache_accessibility_indicators():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
geomloc = geom.loc if geom_type == CensusTract else geom.geom
blocks = CensusBlock.objects.filter(loc__dwithin=(geomloc, 5280))
retval = 0.0
for block in blocks:
for emp in block.censusblockemployment_set.all():
retval += emp.jobs
pop = IndicatorCache.objects.get(indicator_name='pop', \
area_type=geom_str, area_id=geom.id).indicator_value
if pop == 0: continue
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='jobs_within_mile_pc',\
indicator_value = retval/float(pop))
cv.save()
def cache_construction_indicator():
lastyear = cst.localize(datetime.datetime.now() -
datetime.timedelta(days=365.25))
city_geom = Municipality.objects.get(name='Chicago').geom
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
pop = IndicatorCache.objects.get(indicator_name='pop', \
area_type=geom_str, area_id=geom.id).indicator_value
if pop == 0: continue
if (geom_type == CensusTract):
if not city_geom.intersects(geom.loc): continue
myinds = BuildingPermit.objects.filter(loc__within=geom.loc).\
filter(timestamp__gte=lastyear)
retval = 0
for myind in myinds:
if myind.cost is not None: retval += myind.cost
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='construction_pc',\
indicator_value = float(retval)/pop,\
indicator_date = lastyear)
cv.save()
else:
if not city_geom.intersects(geom.geom): continue
if geom_type == Municipality and geom.name != 'Chicago':
continue
myinds = BuildingPermit.objects.filter(loc__within=geom.geom).\
filter(timestamp__gte=lastyear)
retval = 0
for myind in myinds:
if myind.cost is not None: retval += myind.cost
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='construction_pc',\
indicator_value = float(retval)/pop,\
indicator_date = lastyear)
cv.save()
def cache_landuse_indicators():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
allpins, sfh, condo, multi, commind = None, None, None, None, None
if (geom_type==CensusTract):
allpins = Assessor.objects.filter(loc__within=geom.loc)
sfh = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__exact=1)
condo = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__exact=2)
multi = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__in=(3,4))
commind = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__exact=(5))
else:
allpins = Assessor.objects.filter(loc__within=geom.geom)
sfh = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__exact=1)
condo = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__exact=2)
multi = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__in=(3,4))
commind = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__exact=(5))
tot_pins = len(allpins)
if tot_pins == 0: continue
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_sfh',\
indicator_value = float(len(sfh))/len(allpins)*100)
cv.save()
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_condo',\
indicator_value = float(len(condo))/len(allpins)*100)
cv.save()
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_multifamily',\
indicator_value = float(len(multi))/len(allpins)*100)
cv.save()
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_commind',\
indicator_value = float(len(commind))/len(allpins)*100)
cv.save()
def cache_landuse_indicators():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
allpins, sfh, condo, multi, commind = None, None, None, None, None
if (geom_type == CensusTract):
allpins = Assessor.objects.filter(loc__within=geom.loc)
sfh = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__exact=1)
condo = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__exact=2)
multi = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__in=(3,4))
commind = Assessor.objects.filter(loc__within=geom.loc).\
filter(ptype_id__exact=(5))
else:
allpins = Assessor.objects.filter(loc__within=geom.geom)
sfh = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__exact=1)
condo = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__exact=2)
multi = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__in=(3,4))
commind = Assessor.objects.filter(loc__within=geom.geom).\
filter(ptype_id__exact=(5))
tot_pins = len(allpins)
if tot_pins == 0: continue
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_sfh',\
indicator_value = float(len(sfh))/len(allpins)*100)
cv.save()
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_condo',\
indicator_value = float(len(condo))/len(allpins)*100)
cv.save()
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_multifamily',\
indicator_value = float(len(multi))/len(allpins)*100)
cv.save()
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='pct_commind',\
indicator_value = float(len(commind))/len(allpins)*100)
cv.save()
def cache_construction_indicator():
lastyear = cst.localize(datetime.datetime.now() - datetime.timedelta(days=365.25))
city_geom=Municipality.objects.get(name='Chicago').geom
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
pop = IndicatorCache.objects.get(indicator_name='pop', \
area_type=geom_str, area_id=geom.id).indicator_value
if pop==0: continue
if (geom_type==CensusTract):
if not city_geom.intersects(geom.loc): continue
myinds = BuildingPermit.objects.filter(loc__within=geom.loc).\
filter(timestamp__gte=lastyear)
retval = 0
for myind in myinds:
if myind.cost is not None: retval += myind.cost
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='construction_pc',\
indicator_value = float(retval)/pop,\
indicator_date = lastyear)
cv.save()
else:
if not city_geom.intersects(geom.geom): continue
if geom_type==Municipality and geom.name!='Chicago': continue
myinds = BuildingPermit.objects.filter(loc__within=geom.geom).\
filter(timestamp__gte=lastyear)
retval = 0
for myind in myinds:
if myind.cost is not None: retval += myind.cost
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='construction_pc',\
indicator_value = float(retval)/pop,\
indicator_date = lastyear)
cv.save()
def cache_market_indicators():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
print '.'
retval = get_foreclosure_rate(geoms=geom)
for k, v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='foreclosure_rate',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_median_price(geoms=geom)
for k, v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='median_price',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_transactions_per_thousand(geoms=geom)
for k, v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='transactions_per_thousand',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_mortgages_per_thousand(geoms=geom)
for k, v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='mortgages_per_thousand',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_percent_lowvalue_transactions(geoms=geom)
for k, v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='percent_lowvalue',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_percent_business_buyers(geoms=geom)
for k, v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='percent_businessbuyers',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
def cache_market_indicators():
for geom_type,geom_str in \
zip([CensusTract,Municipality,Ward,CommunityArea],\
['Census Tract', 'Municipality', 'Ward', 'Community Area']):
for geom in geom_type.objects.all():
print '.'
retval = get_foreclosure_rate(geoms=geom)
for k,v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='foreclosure_rate',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_median_price(geoms=geom)
for k,v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='median_price',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_transactions_per_thousand(geoms=geom)
for k,v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='transactions_per_thousand',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_mortgages_per_thousand(geoms=geom)
for k,v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='mortgages_per_thousand',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_percent_lowvalue_transactions(geoms=geom)
for k,v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='percent_lowvalue',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()
retval = get_percent_business_buyers(geoms=geom)
for k,v in retval.iteritems():
cv = IndicatorCache(\
area_type=geom_str, area_id = geom.id,\
indicator_name='percent_businessbuyers',\
indicator_value = v,\
indicator_date = quarter_to_datetime(k))
cv.save()